“Analytics is a journey and not a destination!! It takes considerable effort to frame that journey and execute it with a sense of purpose. You will encounter stumbling blocks that may threaten your initiative but you need to find a way out and keep marching ahead.”


How is it like to build a data analytics strategy?

We did a data analytics exercise for a US client recently in education domain that had all the flavors of roadblocks one can encounter on venturing into analytics territory. I intend to summarize those here along with solutions we found in collaboration with all stakeholders


  • This one is most worrisome and showstopper pretty much everywhere.  Data Quality and Data Integrity!!! Many of the fields    had inconsistent data, nearly 50%. Ones that had data were ambushed by missing values. At the same time data integrity was questioned. Different departments came out with  different numbers for same metrics. That’s terrifying indeed! We reconciled  data across different systems. Though we never had exact match however, we narrowed down differences to less  than 10 %. All agreed to a 10% tolerance level. Good to go!


  • Same metrics, different understanding! How do we define a conversion etc?  This wasn’t anticipated to be honest but became clear after couple of stakeholder meetings. Good part we had every stakeholder from finance, sales, marketing and operations team on board. We were able to identify and close understanding gaps quickly.


  1. Excitement and Expectations! Kicking of data analytics initiatives generated quite an excitement for all. Everyone wanted a pie of magic (isn’t that expect out of analytics these days) at the end. At this stage it was critical to set priorities right. Even though we started with defined set of activities, things changed in weeks time when client presented alternative priorities. Client’s executive team played an important role here in clearly defining what their priorities were what they really wanted and how they will act upon the insights we deliver. I would say clear understanding and desire on client’s part won the battle here.


  • Define your data quality tolerance limits. You never will have 100% correct data. However this shouldn’t stop you from acting.
  • Having all stakeholders who are linked to the project on board. This may be executive team, sales, marketing or operations team. This is where you can identify inconsistent understanding and close gaps.
  • Be clear and precise about what you want and how you plan data analytics to act upon insights.


This was just a month’s exercise. Surely we will hit many such scenarios ahead.


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